1. Field of the Invention
The present invention relates to a reprographic system comprising at least one sensor, providing a sensor signal, at least one actuator, responsive to an actuator signal, and a control unit for generating the actuator signal for the at least one actuator in dependence on the sensor signal of the at least one sensor
2. Description of Background Art
In many cases, complex systems such as reprographic systems are required to make trade-offs between important characteristics of the system such as warm-up time, speed, and power consumption. Most of the time these characteristics, further to be indicated as “system characteristics,” are established when the system is designed. However such trade-offs heavily depend on the environment where the reprographic system eventually will be used. Therefore, it is desirable that the control of the system should adapt the system dynamically. Failure to respond adequately to changing environments might result in the occurrence of faults.
Nowadays, current controllers for reprographic machines are not able to adapt to various circumstances. Most of the time, another controller for that circumstance is needed to cope with other circumstances.
Adaptive control as such is known in the art. In this respect, adaptability is defined as a dynamic in-product trade-off between characteristics of the system at system level.
Several approaches to realize adaptive control exist. According to a first approach, Model Reference Adaptive Control (MRAC) uses a reference model that reflects the desired behavior of the system. On the basis of the output of the reference model and the observations, the controller is tuned. A second approach considers a type of adaptive controllers, so called self-tuning controllers (STC), which estimate the correct parameters of the system based on observations and tunes the control accordingly. In the last few decades, techniques from the area of artificial intelligence (AI), such as rule-based systems, fuzzy logic, neural networks, evolutionary algorithms, etc. have been used in order to predict the right control parameters. A drawback of some of these techniques, such as neural networks, is that such techniques do not provide any insight in why the machine changes its behavior. This is because such models are ‘black-box’ models, which make the diagnostics and explanation of the behavior of a machine cumbersome. Furthermore, rules of fuzzy logic sentences are difficult to obtain and require extensive testing in order to handle all the relevant situations.
It is desirable to be able to realize a controller for a reprographic machine that is adaptive.